#' eXtreme Gradient Boosting Training
#'
#' \code{xgb.train} is an advanced interface for training an xgboost model.
#' The \code{xgboost} function is a simpler wrapper for \code{xgb.train}.
#'
#' @param params the list of parameters. The complete list of parameters is
#'   available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
#'   is a shorter summary:
#'
#' 1. General Parameters
#'
#' \itemize{
#'   \item \code{booster} which booster to use, can be \code{gbtree} or \code{gblinear}. Default: \code{gbtree}.
#' }
#'
#' 2. Booster Parameters
#'
#' 2.1. Parameters for Tree Booster
#'
#' \itemize{
#'   \item{ \code{eta} control the learning rate: scale the contribution of each tree by a factor of \code{0 < eta < 1}
#'          when it is added to the current approximation.
#'          Used to prevent overfitting by making the boosting process more conservative.
#'          Lower value for \code{eta} implies larger value for \code{nrounds}: low \code{eta} value means model
#'          more robust to overfitting but slower to compute. Default: 0.3}
#'   \item{ \code{gamma} minimum loss reduction required to make a further partition on a leaf node of the tree.
#'          the larger, the more conservative the algorithm will be.}
#'   \item \code{max_depth} maximum depth of a tree. Default: 6
#'   \item{\code{min_child_weight} minimum sum of instance weight (hessian) needed in a child.
#'         If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight,
#'         then the building process will give up further partitioning.
#'         In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node.
#'         The larger, the more conservative the algorithm will be. Default: 1}
#'   \item{ \code{subsample} subsample ratio of the training instance.
#'         Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees
#'         and this will prevent overfitting. It makes computation shorter (because less data to analyse).
#'         It is advised to use this parameter with \code{eta} and increase \code{nrounds}. Default: 1}
#'   \item \code{colsample_bytree} subsample ratio of columns when constructing each tree. Default: 1
#'   \item \code{lambda} L2 regularization term on weights. Default: 1
#'   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
#'   \item{ \code{num_parallel_tree} Experimental parameter. number of trees to grow per round.
#'          Useful to test Random Forest through XGBoost
#'          (set \code{colsample_bytree < 1}, \code{subsample  < 1}  and \code{round = 1}) accordingly.
#'          Default: 1}
#'   \item{ \code{monotone_constraints} A numerical vector consists of \code{1}, \code{0} and \code{-1} with its length
#'          equals to the number of features in the training data.
#'          \code{1} is increasing, \code{-1} is decreasing and \code{0} is no constraint.}
#'   \item{ \code{interaction_constraints} A list of vectors specifying feature indices of permitted interactions.
#'        Each item of the list represents one permitted interaction where specified features are allowed to interact with each other.
#'        Feature index values should start from \code{0} (\code{0} references the first column).
#'        Leave argument unspecified for no interaction constraints.}
#' }
#'
#' 2.2. Parameters for Linear Booster
#'
#' \itemize{
#'   \item \code{lambda} L2 regularization term on weights. Default: 0
#'   \item \code{lambda_bias} L2 regularization term on bias. Default: 0
#'   \item \code{alpha} L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
#' }
#'
#' 3. Task Parameters
#'
#' \itemize{
#' \item{ \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it.
#'        The default objective options are below:
#'   \itemize{
#'     \item \code{reg:squarederror} Regression with squared loss (Default).
#'     \item{ \code{reg:squaredlogerror}: regression with squared log loss \eqn{1/2 * (log(pred + 1) - log(label + 1))^2}.
#'            All inputs are required to be greater than -1.
#'            Also, see metric rmsle for possible issue with this objective.}
#'     \item \code{reg:logistic} logistic regression.
#'     \item \code{reg:pseudohubererror}: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
#'     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
#'     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
#'     \item \code{binary:hinge}: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
#'     \item{ \code{count:poisson}: Poisson regression for count data, output mean of Poisson distribution.
#'            \code{max_delta_step} is set to 0.7 by default in poisson regression (used to safeguard optimization).}
#'     \item{ \code{survival:cox}: Cox regression for right censored survival time data (negative values are considered right censored).
#'            Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional
#'            hazard function \code{h(t) = h0(t) * HR)}.}
#'     \item{ \code{survival:aft}: Accelerated failure time model for censored survival time data. See
#'            \href{https://xgboost.readthedocs.io/en/latest/tutorials/aft_survival_analysis.html}{Survival Analysis with Accelerated Failure Time}
#'            for details.}
#'     \item \code{aft_loss_distribution}: Probability Density Function used by \code{survival:aft} and \code{aft-nloglik} metric.
#'     \item{ \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective.
#'            Class is represented by a number and should be from 0 to \code{num_class - 1}.}
#'     \item{ \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be
#'            further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging
#'            to each class.}
#'     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
#'     \item{ \code{rank:ndcg}: Use LambdaMART to perform list-wise ranking where
#'            \href{https://en.wikipedia.org/wiki/Discounted_cumulative_gain}{Normalized Discounted Cumulative Gain (NDCG)} is maximized.}
#'     \item{ \code{rank:map}: Use LambdaMART to perform list-wise ranking where
#'            \href{https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision}{Mean Average Precision (MAP)}
#'            is maximized.}
#'     \item{ \code{reg:gamma}: gamma regression with log-link.
#'            Output is a mean of gamma distribution.
#'            It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be
#'            \href{https://en.wikipedia.org/wiki/Gamma_distribution#Applications}{gamma-distributed}.}
#'     \item{ \code{reg:tweedie}: Tweedie regression with log-link.
#'            It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be
#'            \href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.}
#'   }
#'  }
#'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
#'   \item{ \code{eval_metric} evaluation metrics for validation data.
#'          Users can pass a self-defined function to it.
#'          Default: metric will be assigned according to objective
#'          (rmse for regression, and error for classification, mean average precision for ranking).
#'          List is provided in detail section.}
#' }
#'
#' @param data training dataset. \code{xgb.train} accepts only an \code{xgb.DMatrix} as the input.
#'        \code{xgboost}, in addition, also accepts \code{matrix}, \code{dgCMatrix}, or name of a local data file.
#' @param nrounds max number of boosting iterations.
#' @param watchlist named list of xgb.DMatrix datasets to use for evaluating model performance.
#'        Metrics specified in either \code{eval_metric} or \code{feval} will be computed for each
#'        of these datasets during each boosting iteration, and stored in the end as a field named
#'        \code{evaluation_log} in the resulting object. When either \code{verbose>=1} or
#'        \code{\link{cb.print.evaluation}} callback is engaged, the performance results are continuously
#'        printed out during the training.
#'        E.g., specifying \code{watchlist=list(validation1=mat1, validation2=mat2)} allows to track
#'        the performance of each round's model on mat1 and mat2.
#' @param obj customized objective function. Returns gradient and second order
#'        gradient with given prediction and dtrain.
#' @param feval customized evaluation function. Returns
#'        \code{list(metric='metric-name', value='metric-value')} with given
#'        prediction and dtrain.
#' @param verbose If 0, xgboost will stay silent. If 1, it will print information about performance.
#'        If 2, some additional information will be printed out.
#'        Note that setting \code{verbose > 0} automatically engages the
#'        \code{cb.print.evaluation(period=1)} callback function.
#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
#'        Default is 1 which means all messages are printed. This parameter is passed to the
#'        \code{\link{cb.print.evaluation}} callback.
#' @param early_stopping_rounds If \code{NULL}, the early stopping function is not triggered.
#'        If set to an integer \code{k}, training with a validation set will stop if the performance
#'        doesn't improve for \code{k} rounds.
#'        Setting this parameter engages the \code{\link{cb.early.stop}} callback.
#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
#'        then this parameter must be set as well.
#'        When it is \code{TRUE}, it means the larger the evaluation score the better.
#'        This parameter is passed to the \code{\link{cb.early.stop}} callback.
#' @param save_period when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
#'        0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.
#' @param save_name the name or path for periodically saved model file.
#' @param xgb_model a previously built model to continue the training from.
#'        Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
#'        file with a previously saved model.
#' @param callbacks a list of callback functions to perform various task during boosting.
#'        See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
#'        parameters' values. User can provide either existing or their own callback methods in order
#'        to customize the training process.
#' @param ... other parameters to pass to \code{params}.
#' @param label vector of response values. Should not be provided when data is
#'        a local data file name or an \code{xgb.DMatrix}.
#' @param missing by default is set to NA, which means that NA values should be considered as 'missing'
#'        by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
#'        This parameter is only used when input is a dense matrix.
#' @param weight a vector indicating the weight for each row of the input.
#'
#' @details
#' These are the training functions for \code{xgboost}.
#'
#' The \code{xgb.train} interface supports advanced features such as \code{watchlist},
#' customized objective and evaluation metric functions, therefore it is more flexible
#' than the \code{xgboost} interface.
#'
#' Parallelization is automatically enabled if \code{OpenMP} is present.
#' Number of threads can also be manually specified via \code{nthread} parameter.
#'
#' The evaluation metric is chosen automatically by XGBoost (according to the objective)
#' when the \code{eval_metric} parameter is not provided.
#' User may set one or several \code{eval_metric} parameters.
#' Note that when using a customized metric, only this single metric can be used.
#' The following is the list of built-in metrics for which XGBoost provides optimized implementation:
#'   \itemize{
#'      \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
#'      \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
#'      \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
#'      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
#'            Different threshold (e.g., 0.) could be specified as "error@0."
#'      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#'      \item \code{mae} Mean absolute error
#'      \item \code{mape} Mean absolute percentage error
#'      \item{ \code{auc} Area under the curve.
#'             \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.}
#'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
#'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
#'   }
#'
#' The following callbacks are automatically created when certain parameters are set:
#' \itemize{
#'   \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
#'         and the \code{print_every_n} parameter is passed to it.
#'   \item \code{cb.evaluation.log} is on when \code{watchlist} is present.
#'   \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
#'   \item \code{cb.save.model}: when \code{save_period > 0} is set.
#' }
#'
#' @return
#' An object of class \code{xgb.Booster} with the following elements:
#' \itemize{
#'   \item \code{handle} a handle (pointer) to the xgboost model in memory.
#'   \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
#'   \item \code{niter} number of boosting iterations.
#'   \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
#'         first column corresponding to iteration number and the rest corresponding to evaluation
#'         metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
#'   \item \code{call} a function call.
#'   \item \code{params} parameters that were passed to the xgboost library. Note that it does not
#'         capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
#'   \item \code{callbacks} callback functions that were either automatically assigned or
#'         explicitly passed.
#'   \item \code{best_iteration} iteration number with the best evaluation metric value
#'         (only available with early stopping).
#'   \item \code{best_score} the best evaluation metric value during early stopping.
#'         (only available with early stopping).
#'   \item \code{feature_names} names of the training dataset features
#'         (only when column names were defined in training data).
#'   \item \code{nfeatures} number of features in training data.
#' }
#'
#' @seealso
#' \code{\link{callbacks}},
#' \code{\link{predict.xgb.Booster}},
#' \code{\link{xgb.cv}}
#'
#' @references
#'
#' Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System",
#' 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, \url{https://arxiv.org/abs/1603.02754}
#'
#' @examples
#' data(agaricus.train, package='xgboost')
#' data(agaricus.test, package='xgboost')
#'
#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
#' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
#' watchlist <- list(train = dtrain, eval = dtest)
#'
#' ## A simple xgb.train example:
#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
#'               objective = "binary:logistic", eval_metric = "auc")
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
#'
#'
#' ## An xgb.train example where custom objective and evaluation metric are used:
#' logregobj <- function(preds, dtrain) {
#'    labels <- getinfo(dtrain, "label")
#'    preds <- 1/(1 + exp(-preds))
#'    grad <- preds - labels
#'    hess <- preds * (1 - preds)
#'    return(list(grad = grad, hess = hess))
#' }
#' evalerror <- function(preds, dtrain) {
#'   labels <- getinfo(dtrain, "label")
#'   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
#'   return(list(metric = "error", value = err))
#' }
#'
#' # These functions could be used by passing them either:
#' #  as 'objective' and 'eval_metric' parameters in the params list:
#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
#'               objective = logregobj, eval_metric = evalerror)
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
#'
#' #  or through the ... arguments:
#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#'                  objective = logregobj, eval_metric = evalerror)
#'
#' #  or as dedicated 'obj' and 'feval' parameters of xgb.train:
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#'                  obj = logregobj, feval = evalerror)
#'
#'
#' ## An xgb.train example of using variable learning rates at each iteration:
#' param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
#'               objective = "binary:logistic", eval_metric = "auc")
#' my_etas <- list(eta = c(0.5, 0.1))
#' bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
#'                  callbacks = list(cb.reset.parameters(my_etas)))
#'
#' ## Early stopping:
#' bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
#'                  early_stopping_rounds = 3)
#'
#' ## An 'xgboost' interface example:
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
#'                max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
#'                objective = "binary:logistic")
#' pred <- predict(bst, agaricus.test$data)
#'
#' @rdname xgb.train
#' @export
xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
                      obj = NULL, feval = NULL, verbose = 1, print_every_n = 1L,
                      early_stopping_rounds = NULL, maximize = NULL,
                      save_period = NULL, save_name = "xgboost.model",
                      xgb_model = NULL, callbacks = list(), ...) {

  check.deprecation(...)

  params <- check.booster.params(params, ...)

  check.custom.obj()
  check.custom.eval()

  # data & watchlist checks
  dtrain <- data
  if (!inherits(dtrain, "xgb.DMatrix"))
    stop("second argument dtrain must be xgb.DMatrix")
  if (length(watchlist) > 0) {
    if (typeof(watchlist) != "list" ||
        !all(vapply(watchlist, inherits, logical(1), what = 'xgb.DMatrix')))
      stop("watchlist must be a list of xgb.DMatrix elements")
    evnames <- names(watchlist)
    if (is.null(evnames) || any(evnames == ""))
      stop("each element of the watchlist must have a name tag")
  }
  # Handle multiple evaluation metrics given as a list
  for (m in params$eval_metric) {
    params <- c(params, list(eval_metric = m))
  }

  # evaluation printing callback
  params <- c(params)
  print_every_n <- max(as.integer(print_every_n), 1L)
  if (!has.callbacks(callbacks, 'cb.print.evaluation') &&
      verbose) {
    callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n))
  }
  # evaluation log callback:  it is automatically enabled when watchlist is provided
  evaluation_log <- list()
  if (!has.callbacks(callbacks, 'cb.evaluation.log') &&
      length(watchlist) > 0) {
    callbacks <- add.cb(callbacks, cb.evaluation.log())
  }
  # Model saving callback
  if (!is.null(save_period) &&
      !has.callbacks(callbacks, 'cb.save.model')) {
    callbacks <- add.cb(callbacks, cb.save.model(save_period, save_name))
  }
  # Early stopping callback
  stop_condition <- FALSE
  if (!is.null(early_stopping_rounds) &&
      !has.callbacks(callbacks, 'cb.early.stop')) {
    callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
                                                 maximize = maximize, verbose = verbose))
  }

  # Sort the callbacks into categories
  cb <- categorize.callbacks(callbacks)
  params['validate_parameters'] <- TRUE
  if (!is.null(params[['seed']])) {
    warning("xgb.train: `seed` is ignored in R package.  Use `set.seed()` instead.")
  }

  # The tree updating process would need slightly different handling
  is_update <- NVL(params[['process_type']], '.') == 'update'

  # Construct a booster (either a new one or load from xgb_model)
  handle <- xgb.Booster.handle(params, append(watchlist, dtrain), xgb_model)
  bst <- xgb.handleToBooster(handle)

  # extract parameters that can affect the relationship b/w #trees and #iterations
  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
  num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)

  # When the 'xgb_model' was set, find out how many boosting iterations it has
  niter_init <- 0
  if (!is.null(xgb_model)) {
    niter_init <- as.numeric(xgb.attr(bst, 'niter')) + 1
    if (length(niter_init) == 0) {
      niter_init <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
    }
  }
  if (is_update && nrounds > niter_init)
    stop("nrounds cannot be larger than ", niter_init, " (nrounds of xgb_model)")

  niter_skip <- ifelse(is_update, 0, niter_init)
  begin_iteration <- niter_skip + 1
  end_iteration <- niter_skip + nrounds

  # the main loop for boosting iterations
  for (iteration in begin_iteration:end_iteration) {

    for (f in cb$pre_iter) f()

    xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)

    if (length(watchlist) > 0)
      bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)  # nolint: object_usage_linter

    xgb.attr(bst$handle, 'niter') <- iteration - 1

    for (f in cb$post_iter) f()

    if (stop_condition) break
  }
  for (f in cb$finalize) f(finalize = TRUE)

  bst <- xgb.Booster.complete(bst, saveraw = TRUE)

  # store the total number of boosting iterations
  bst$niter <- end_iteration

  # store the evaluation results
  if (length(evaluation_log) > 0 &&
      nrow(evaluation_log) > 0) {
    # include the previous compatible history when available
    if (inherits(xgb_model, 'xgb.Booster') &&
        !is_update &&
        !is.null(xgb_model$evaluation_log) &&
        isTRUE(all.equal(colnames(evaluation_log),
                         colnames(xgb_model$evaluation_log)))) {
      evaluation_log <- rbindlist(list(xgb_model$evaluation_log, evaluation_log))
    }
    bst$evaluation_log <- evaluation_log
  }

  bst$call <- match.call()
  bst$params <- params
  bst$callbacks <- callbacks
  if (!is.null(colnames(dtrain)))
    bst$feature_names <- colnames(dtrain)
  bst$nfeatures <- ncol(dtrain)

  return(bst)
}
